1. Introduction

Australia has a long history of severe droughts spanning multiple years. Droughts such as the Federation drought, the WWII drought, and the Millennium drought spanned years, caused widespread agricultural collapse, and led to increased bushfires. The drought from 2017-2019 produced an unprecedented bushfire season dubbed “The Black Summer” where XYZ ha burned and enough smoke was produced to reach Argentina. Prior to the fires, widespread leaf abscission is most likely to have acted as a precursor to produce the fuel layer for the 2019 bushfires.

Prior to the drought of 2017-2019, the Millennium Drought had been the worst Australian drought in methodologically observed record (van Dijk et al., 2013). However unlike the 2017-2019 drought, the Millennium Drought did not produce bushfires comparable in scale to those of the Black Summer. Here we ask what happened to Australian forests and woodlands in the 2017-2019 drought, that did not happen in prior observed severe droughts?

It is pertinent to note that Australian trees are extraordinarily well adapted to drought. Australian trees exist across extensive climate gradient from energy limited tropical and temperate rainforests to water limited woodlands of the interior. The vast majority of Australian forests and woodlands are water limited, which is true even for the tall Eucalyptus dominated forests of the eastern coast (source; Givnish?). A key determinant of where and what trees can grow is the balance between precipitation (P) and potential evapotranspiration (PET). The ratio of P:PET and similar variants are routed in Budkyo’s energy limitation framework (source) where actual evapotranspiration is predominantly ultimately limited by solar radiation or precipitation. Temperate and tropical rainforests dominate the forested regions of eastern Australia where P:PET is less than 1.

The Australian forest and woodland landscape is not only dominated by PET, but the distribution of P has unusually high interannual variability owing to strong teleconnections with sea surface temperature anomalies (source).

Despite the unusual drought adapted resilliance of Australian trees, widespread tree mortality was reported during the 2017-2019 drought (Dead tree detective link). - Donohue et al., 2009
- Budyko energy limitation framework
- The rate of climate change in eastern Australia

Questions:
1. Has meteorological drought severity changed through time?
2. Have vegetation drought responses changed through time?
3. Why did forest vegetation respond more to the 2017-2019 drought leading to the “Black Summer” than prior droughts of similar severity?

2. Methods

2.1 Study region

We focus on eastern Australian vegetation containing trees. Specifically we use the National Vegetation Information System (NVIS) major classes to subset eastern Australia into the focal study regions. These classes are predominantly dominated by trees in the Acacia, Callitris, Casuarina, Eucalyptus, Mallee, Melaleuca genera.

NVIS Major Vegetation Classes that contain trees.

NVIS Major Vegetation Classes that contain trees.

Mean Annual NIR-V

Mean Annual NIR-V

2.2 Regional climate

The study region spans a vast gradient in mean annual precipitation (130 - 4250 mm yr^-1) and mean annual potential evapotranspiration (250 - 2100 mm yr^-1).


Mean Annual Climate of Forests and Woodlands

Mean Annual Climate of Forests and Woodlands

2.3 Models

We examined the effects of P, PET, and P:PET upon \(NIR_{V}\) by using generalized additive models (Wood 2017). \[NIR_{V_{i}} = \beta_0 + f_1(P_i) + f_2(PET_i) + \epsilon_i\] Where \(\epsilon\) is assumed to be identically and independently distributed \(\epsilon \sim N(0,\sigma^2)\). \(f_1\) and \(f_2\) represent smoothing functions that approximate a sequence of basis functions \[f(x) = \sum_{j = 1}^{J} b_{j}(x)\beta_{j}\] (Wood 2017). The smoothing functions are penalized by a \(\lambda\) term to maximize the log-likelihood. To further minimize the potential for overfitting the smoothing functions we specify a maximum number of basis functions for the smoothing function to approximate. Further detail behind the theory of generalized additive models can be found in Wood (2017).

3. Results

Question 1: Has meteorological drought severity changed through time?

Please caption every figure

Please caption every figure


### Question 2: Have vegetation drought responses changed through time?

##    user  system elapsed 
##  42.652   5.547  22.482
Long-term

Long-term



[“../”]

NVIS Vegetation Class Mean NIRV Range NIRV R2 RMSE Long-term Trend
Acacia Forests and Woodlands 0.06 0.18 0.47 0.01 -0.00014
Acacia Open Woodlands 0.06 0.08 0.13 0.01 -0.00024
Callitris Forests and Woodlands 0.08 0.14 0.59 0.01 0.00001
Casuarina Forests and Woodlands 0.06 0.14 0.00 0.03 -0.00040
Eucalypt Low Open Forests 0.09 0.15 0.17 0.03 -0.00008
Eucalypt Open Forests 0.11 0.25 0.37 0.02 0.00017
Eucalypt Open Woodlands 0.07 0.24 0.67 0.01 0.00002
Eucalypt Tall Open Forests 0.13 0.21 0.41 0.02 0.00051
Eucalypt Woodlands 0.09 0.25 0.63 0.01 0.00008
Low Closed Forests and Tall Closed Shrublands 0.07 0.16 0.19 0.04 -0.00033
Mallee Woodlands and Shrublands 0.07 0.17 0.30 0.01 -0.00003
Melaleuca Forests and Woodlands 0.07 0.18 0.34 0.01 0.00002
Other Forests and Woodlands 0.08 0.19 0.52 0.02 -0.00001
Rainforests and Vine Thickets 0.13 0.23 0.32 0.03 0.00013
Tropical Eucalypt Woodlands/Grasslands 0.08 0.05 0.90 0.01 0.00010

Seasonal means of \(NIR_V\) are well predicted (R2 = 0.72) by a GAM with only climatalogy covariates.

Time of minimum observed NIRV

Time of minimum observed NIRV 

##    user  system elapsed 
## 127.774   0.028 124.889
NIRV Anomaly (sd) sensitivity to standardized anomalies of Precipitation (P) and Potential Evapotranspiration(PET) by season (1983-2019).

NIRV Anomaly (sd) sensitivity to standardized anomalies of Precipitation (P) and Potential Evapotranspiration(PET) by season (1983-2019).

Question 3: Why did forest vegetation respond more to the 2017-2019 drought leading to the “Black Summer” than prior droughts of similar severity?

Which ecosystems were most affected?

Most forest and woodland vegetation classes were experiencing a long-term decline in \(NIR_V\) over the period 1982-2019 (Table 1). Casuarina and Acacia dominated ecosystems experienced the greatest decline while Rainforests and Tall Open Eucalypt Forests experienced the largest long-term increases in \(NIR_V\).

Is forest vegetation more sensitive to meteorological anomalies



## 4. Discussion